Intent Classification of Nepali Factoid Questions

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering Systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches.